Nonlocal Means Image Denoising With Minimum MSE-Based Decay Parameter Adaptation

Yi Zhan,Xuming Zhang,Jinbo Wu,Mingyue Ding

doi:10.1109/access.2019.2937966

Yi Zhan, Xuming Zhang + Show 2 more

Open Access

https://doi.org/10.1109/access.2019.2937966

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Abstract

Nonlocal means (NLM), a patch-based nonlocal recovery paradigm, has attracted much attention in recent decades. The decay parameter will greatly affect restoration performance of the NLM method. However, the existing NLM methods with decay parameter adaptation cannot determine this parameter effectively. To address this problem, we have proposed the minimum mean square error (MSE) based decay parameter adaptation method for the NLM denoising. In the proposed method, the globally optimal decay parameter is determined to produce the pre-denoised image based on the derived relation between the global minimum MSE and the decay parameter. Then, the pixel-wise MSE is estimated based on the pre-denoised result and the corresponding method noise. Finally, the optimal pixel-wise decay parameter is obtained by minimizing the pixel-wise MSE to produce the estimated restored image and the boosting strategy is implemented on this image to generate the final denoised result. Extensive simulations on the standard test images and real images corrupted with Gaussian noise and speckle noise demonstrate that the proposed method significantly outperforms some compared NLM methods in noise reduction and detail preservation and can provide better restoration performance than other state-of-the-art denoising methods in most cases in terms of objective metrics and human vision.

Highlights

Noise will affect image quality, and lead to difficulty in pattern recognition
EXPERIMENTAL RESULTS AND DISCUSSIONS we will firstly verify the effectiveness of the proposed global decay parameter adaptation based nonlocal means (GNLM) and parameter adaptation based nonlocal means (PNLM) methods in determining the optimal decay parameter
The proposed method can adaptively determine the decay parameter for each image pixel by minimizing the pixel-wise mean square error (MSE), which is estimated by approximating the unknown noise-free image and the noise with the global minimum MSE based denoised result and the corresponding method noise, respectively

Summary

Introduction

Noise will affect image quality, and lead to difficulty in pattern recognition. denoising is a crucial step to facilitate subsequent image processing such as segmentation, registration and visualization. Various denoising techniques have been presented for noise removal such as wavelet based methods [1], partial differential equations (PDE) based methods [2], total variation based methods [3], [4], nonlocal means (NLM) methods [5] and deep learning based methods [6]. Among these methods, the NLM method has attracted much attention in the field of image denoising. This method was originally designed for Gaussian noise reduction, and it has been extended to suppress speckle noise inherent in ultrasound (US) imaging and synthetic aperture radar (SAR) imaging.

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